Dean Rossano Final Project

# load packages
library(tidyverse)

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## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
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library(plotly)

## 
## Attaching package: 'plotly'
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## The following object is masked from 'package:ggplot2':
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library(sf)

## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE

library(ggspatial)
library("viridis") 

## Loading required package: viridisLite

# load data
data = read_rds("state_crime_unemp.rds")

# filter data to specified states
filtered_data <- filter(data, NAME ==  "Illinois" | NAME == "California" | 
                          NAME == "Idaho" | NAME == "Indiana")

# plot unemployment over time
unemployment_plot <-  plot_ly(filtered_data, x = ~Year, y = ~unemp_rate, 
                              color = ~NAME) %>% 
                              add_lines()

unemployment_plot<- unemployment_plot %>% layout(title = 'Unemployment Rate over Time',
  xaxis = list(title = 'Year'),
  yaxis = list (title = 'Unemployment Rate'))

unemployment_plot

# plot crime over time
crime_plot <-  plot_ly(filtered_data, x = ~Year, y = ~crime_rate, 
                              color = ~NAME) %>% 
                              add_lines()

crime_plot<- crime_plot %>% layout(title = 'Crime Rate over Time',
         xaxis = list(title = 'Year'),
         yaxis = list (title = 'Crime Rate'))


crime_plot

# Filter Data to 2014 
data_2014 = filter(data, Year == 2014)

#plot crime vs unemployment scatter plot
crime_vs_unemployment_plot <- plot_ly(filtered_data, x = ~crime_rate, y = ~unemp_rate, color = ~NAME) %>% 
  add_markers()

crime_vs_unemployment_plot <- crime_vs_unemployment_plot %>% layout(title = 'Crime Rate over Time',
         xaxis = list(title = 'Year'),
         yaxis = list (title = 'Crime Rate'))

crime_vs_unemployment_plot

# plot spatial unemployment map
unemp_map = ggplot() +
  geom_sf(data=data_2014,aes(fill=(unemp_rate)))+
  scale_fill_viridis(option = "D")+
  annotation_scale(location = "bl")+   #0.002, upper,and 0.05 lower
  annotation_north_arrow(location = "br", which_north = "true", 
                         style = north_arrow_fancy_orienteering) +
  guides(fill=guide_legend(title="Unemployment Rate in 2014"))+
  xlab("Longitude") + ylab("Latitude") +
  ggtitle("Unemployment Rate Map Over Contiguous US") 

unemp_map

## Scale on map varies by more than 10%, scale bar may be inaccurate

# plot spatial crime map
crime_map = ggplot() +
  geom_sf(data=data_2014,aes(fill=crime_rate))+
  scale_fill_viridis()+
  annotation_scale(location = "bl")+   #0.002, upper,and 0.05 lower
  annotation_north_arrow(location = "br", which_north = "true", 
                         style = north_arrow_fancy_orienteering) +
  guides(fill=guide_legend(title="Crime Rate in 2014"))+
  xlab("Longitude") + ylab("Latitude") +
  ggtitle("Crime Rate Map Over Contiguous US") 

crime_map

## Scale on map varies by more than 10%, scale bar may be inaccurate